Multi-Objective Complete Fuzzy Clustering Approach. Issue 2 (3rd April 2017)

• Record Type: Journal Article
• Authors: Shahsamandi E., Parastou; Sadi-nezhad, Soheil; Saghaei, Abbas
• Is Part Of: Intelligent automation & soft computing. Volume 23:Issue 2(2017)
• Page Start: 285
• Page End: 294
• Publication Date: 2017-04-03
• Subjects: Fuzzy clustering -- Multi-objective optimization -- NSGA-II algorithm -- ε-constraint method -- Fuzzy data -- Cluster connectedness -- Cluster compactness -- Cluster separation; Artificial intelligence -- Periodicals; Intelligent control systems -- Periodicals; 003.5
• DOI: 10.1080/10798587.2016.1209322
• Languages: English
• ISSNs: 1079-8587

Abstract

Abstract: The process of data clustering has mainly focused on optimizing a single objective function, and thus, some information is not used for clustering. Therefore, the aim of this study is to propose a multi-objective complete fuzzy clustering model (MoCFC) that simultaneously optimizes data compactness, separation, and connectedness. The model employs two optimization algorithms; AUGMECON and NSGA-II. Using some fuzzy datasets, the results show that AUGMECON has lower convergence and coverage than NSGA-II, but a higher success index. Moreover, in terms of various cluster validity indices, AUGMECON achieves better performance. However, NSGA-II is the better choice if execution time is critical.